Our position on why production AI in sensitive workflows needs visible evaluation thresholds, guardrails, and a human-review trail — not just convincing output.
Read the perspective
Our delivery studio in Rawlins, Wyoming is open, and every engagement now runs through one framework: production AI, shipped with its evidence attached.
A standalone assurance program: we review your systems and the third parties you already rely on, with no platform to sell and no conflict of interest.
Our first AI-native engagement reached production behind an eval gate: grounded answers, guardrails green, and two cases routed to a human before delivery.
A hands-on walkthrough of taking an LLM feature from an eval threshold to a guard-railed release — the gates, the evidence, and the rollback plan.
We open up a real delivery: how grounding, evals, guardrails, and human review travelled with the assistant all the way to production.
The measurable difference after modernizing a legacy platform into an AI-ready codebase — observability, reliability, and what shipped next.
Commentary, delivery notes, and event invites. No noise — we send only when we have something worth your attention.
Eval thresholds as a release gate, not a dashboard
Why we treat evaluation scores as a hard gate on shipping — and what changes when the whole team can see them.
What “human-in-the-loop” actually has to mean in production
The difference between a review checkbox and a reviewer who can stop a release — and how to design for the latter.